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Title: Causes and Implications of Persistent Atmospheric Carbon Dioxide Biases in Earth System Models

The strength of feedbacks between a changing climate and future CO2 concentrations are uncertain and difficult to predict using Earth System Models (ESMs). We analyzed emission-driven simulations--in which atmospheric CO2 levels were computed prognostically--for historical (1850-2005) and future periods (RCP 8.5 for 2006-2100) produced by 15 ESMs for the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5). Comparison of ESM prognostic atmospheric CO2 over the historical period with observations indicated that ESMs, on average, had a small positive bias in predictions of contemporary atmospheric CO2. Weak ocean carbon uptake in many ESMs contributed to this bias, based on comparisons with observations of ocean and atmospheric anthropogenic carbon inventories. We found a significant linear relationship between contemporary atmospheric CO2 biases and future CO2 levels for the multi-model ensemble. We used this relationship to create a contemporary CO2 tuned model (CCTM) estimate of the atmospheric CO2 trajectory for the 21st century. The CCTM yielded CO2 estimates of 600 {plus minus} 14 ppm at 2060 and 947 {plus minus} 35 ppm at 2100, which were 21 ppm and 32 ppm below the multi-model mean during these two time periods. Using this emergent constraint approach, the likely ranges of future atmospheric CO2, CO2-inducedmore » radiative forcing, and CO2-induced temperature increases for the RCP 8.5 scenario were considerably narrowed compared to estimates from the full ESM ensemble. Our analysis provided evidence that much of the model-to-model variation in projected CO2 during the 21st century was tied to biases that existed during the observational era, and that model differences in the representation of concentration-carbon feedbacks and other slowly changing carbon cycle processes appear to be the primary driver of this variability. By improving models to more closely match the long-term time series of CO2 from Mauna Loa, our analysis suggests uncertainties in future climate projections can be reduced.« less
 [1] ;  [2] ;  [3] ;  [4] ;  [5] ;  [6] ;  [7] ;  [8] ;  [9] ;  [10] ;  [11] ;  [12] ;  [13] ;  [14] ;  [15] ;  [16]
  1. ORNL
  2. University of California, Irvine
  3. Canadian Centre for Climate Modelling and Analysis, Meteorological Service of Canada
  4. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics
  5. Institut Pierre Simon Laplace, Laboratoire des Sciences du Climat et de l'Environment
  6. State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing
  7. Hadley Centre, U.K. Met Office
  8. Japan Agency for Marine-Earth Science and Technology (JAMSTEC)
  9. Lamont-Doherty Earth Observatory, Columbia University, Palisades, NY
  10. National Center for Atmospheric Research (NCAR)
  11. Meteorological Research Institute, Japan
  12. Princeton University
  13. Max Planck Institute for Meteorology, Hamburg, Germany
  14. Uni Climate, Uni Research
  15. Institute of Numerical Mathematics, Russian Academy of Science, Moscow
  16. China Meteorological Administration (CMA), Beijing
Publication Date:
OSTI Identifier:
DOE Contract Number:
Resource Type:
Journal Article
Resource Relation:
Journal Name: Journal of Geophysical Research: Biogeosciences; Journal Volume: 119; Journal Issue: 2
Research Org:
Oak Ridge National Laboratory (ORNL); Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org:
SC USDOE - Office of Science (SC)
Country of Publication:
United States